NDELS：一种面向夜间去雾、低光照增强与光线抑制的新方法
NDELS: A Novel Approach for Nighttime Dehazing, Low-Light Enhancement, and Light Suppression
AI总结：
针对夜间有雾低光照图像存在的光晕、模糊、色彩失真等质量问题，提出融合去雾、低光增强、光线抑制三流程的NDELS网络，经多数据集多算法对比验证，其在图像质量指标上表现更优。
摘要：
本文致力于解决提升有雾、低光照条件下夜间图像质量这一复杂挑战。克服非均匀照明光晕、纹理模糊、辉光效应、色彩失真、噪声干扰以及整体低光照等问题已被证实极具难度。尽管存在固有难点，本文仍提出了一项开创性解决方案，名为夜间去雾、低光照增强与光线抑制（Nighttime Dehazing, Low-Light Enhancement, and Light Suppression，NDELS）。NDELS采用一种独特的网络，将三个核心流程相结合，以提升可见度、提亮低光照区域，并有效抑制明亮光源产生的眩光。与日间去雾相比，夜间去雾进展有限，而NDELS与之不同，提出了一套全面且创新的方案。研究通过在四个不同数据集上与八种SOTA（state-of-the-art，当前最优）算法进行大量对比，严格验证了NDELS的有效性。实验结果展现了该方法的优越性能，证明其在整体图像质量（包括色彩和边缘增强）方面表现更优。这些结果通过定量指标（PSNR、SSIM）和定性指标（CLIPIQA、MANIQA、TRES）进行衡量。
英文摘要：
This paper tackles the intricate challenge of improving the quality of nighttime images under hazy and low-light conditions. Overcoming issues including nonuniform illumination glows, texture blurring, glow effects, color distortion, noise disturbance, and overall, low light have proven daunting. Despite the inherent difficulties, this paper introduces a pioneering solution named Nighttime Dehazing, Low-Light Enhancement, and Light Suppression (NDELS). NDELS utilizes a unique network that combines three essential processes to enhance visibility, brighten low-light regions, and effectively suppress glare from bright light sources. In contrast to limited progress in nighttime dehazing, unlike its daytime counterpart, NDELS presents a comprehensive and innovative approach. The efficacy of NDELS is rigorously validated through extensive comparisons with eight state-of-the-art algorithms across four diverse datasets. Experimental results showcase the superior performance of our method, demonstrating its outperformance in terms of overall image quality, including color and edge enhancement. Quantitative (PSNR, SSIM) and qualitative metrics (CLIPIQA, MANIQA, TRES), measure these results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
